Название: Deep Learning with PyTorch Step-by-Step: A Beginner's GuideАвтор: Daniel Voigt Godoy
Издательство: Leanpub
Год: 2021
Формат: PDF
Страниц: 1187
Размер: 22,2 Mb
Язык: English
If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it :-)
The book covers from the basics of gradient descent all the way up to fine-tuning large NLP models (BERT and GPT-2) using HuggingFace. It is divided into four parts:
Part I: Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Part II: Computer Vision (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Part III: Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Part IV: Natural Language Processing (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time).
Moreover, this is not a formal book in any way: I am writing this book as if I were having a conversation with you, the reader. I will ask you questions (and give you answers shortly afterward) and I will also make (silly) jokes.